Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
array(0.00804466, dtype=float32)
This seems like a lot of work; there must be an easier way! And there is one,
indeed: We can use item(), for tensors with a single element, or tolist()
otherwise (it still returns a scalar if there is only one element, though).
print(loss.item(), loss.tolist())
Output
0.008044655434787273 0.008044655434787273
At this point, there’s only one piece of code left to change: the predictions. It is
then time to introduce PyTorch’s way of implementing a…
Model
In PyTorch, a model is represented by a regular Python class that inherits from the
Module class.
IMPORTANT: Are you comfortable with object-oriented
programming (OOP) concepts like classes, constructors, methods,
instances, and attributes?
If you’re unsure about any of these terms, I’d strongly
recommend you follow tutorials like Real Python’s "Object-
Oriented Programming (OOP) in Python 3" [48] and "Supercharge
Your Classes With Python super()" [49] before proceeding.
Having a good understanding of OOP is key to benefitting the
most from PyTorch’s capabilities.
So, assuming you’re already comfortable with OOP, let’s dive into developing a
model in PyTorch.
Model | 103